Screen LibraryHead and neck tumor segmentation : first Challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, proceedings /
This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took p...
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| Corporate Authors: | ; |
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| Group Author: | ; ; |
| Published: |
Springer,
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| Publisher Address: | Cham, Switzerland : |
| Publication Dates: | [2021] |
| Literature type: | Book |
| Language: | English |
| Series: |
Lecture notes in computer science,
12603 LNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics |
| Subjects: | |
| Summary: |
This book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results. |
| Carrier Form: | x, 108 pages : illustrations ; 24 cm. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9783030671938 3030671933 |
| Index Number: | RC78 |
| CLC: |
R319-532 R445-37 |
| Call Number: | R445-37/T531/2020 |